Image matting is a process of extracting foreground objects from an image, along with a parameter called an alpha matte. This process leads to useful applications, such as image and video editing, image layer decomposition, and scene analysis. In image matting, a pixel value Ii at a pixel i may be modeled as a linear combination of a foreground color value Fi and a background color value Bi. That is,Ii=αiFi+(1−αi)Bi,  (1)where αi represents the alpha matte value corresponding to opacity of the foreground color.
Conventional image matting techniques can be generally classified into two categories: supervised matting and unsupervised matting. In supervised matting, a user's guidance is provided to label a few pixels to be either “foreground” or “background.” Based on these labeled pixels, a supervised matting method estimates the alpha matte values for remaining unlabeled pixels. In contrast, unsupervised matting aims to automatically estimate the alpha matte from the input image without any user guidance.
Conventional image matting techniques have several drawbacks. First, conventional unsupervised matting techniques are computationally intensive. Although image processing schemes have been proposed to reduce the required computations, these existing schemes may result in degraded image quality.
Second, results produced by conventional image matting techniques may not always be consistent. Lack of global information during the matting process makes it difficult to produce consistent results in dealing with images with cluttered scenes.
Third, conventional unsupervised matting techniques focus on binary partitioning of image content. Since an image may contain more than one foreground object, results generated by these conventional unsupervised matting techniques may not be very practical.